Deep Convective Transport to the Upper-Troposphere/Lower-Stratosphere

到对流层上层/平流层下层的深对流输送

• 批准号: 0918010
• 负责人: Gretchen Mullendore
• 金额: $ 33.32万
• 依托单位: University of North Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0918010&HistoricalAwards=false
• 关键词: Deep; Convective; Transport; Upper; Troposphere

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The gases in the upper troposphere and lower stratosphere (UT/LS) have a significant effect on the chemical and radiative budgets of the global atmosphere, crucial to understanding climate change. To understand the chemical makeup of the UT/LS region, a much better understanding of deep convective transport is needed. Transport in deep convection is unique in its ability to entrain constituents from the near surface and rapidly transport them to the UT/LS region where they are detrained. The variability of entrainment and detrainment in storms is not captured in current climate model convective transport parameterizations.This study proposes to address these problems with a tripartite approach. First, using dual-Doppler velocity data from field campaigns, the observed detrainment profile will be derived for a suite of storms covering a range of storm morphologies and background environments. Second, radar reflectivity data from the same field campaigns will be used to look for relationships between velocity-derived detrainment profiles and the reflectivity fields. The goal of the second part of the proposal is to define an algorithm for processing radar reflectivity observations that will allow for estimates of convective mass transport based on radar reflectivity observations over any region covered by the weather radar network. Third, cloud-resolving models will be used to assess the quality of reflectivity-based transport estimates for specific storms. Using the combination of radar reflectivity observations and modeling, a climatology of convective transport over the central United States will be compiled.Improvements in understanding and modeling of deep convective transport resulting from this study will directly benefit many complementary modeling efforts and improve understanding of convective transport of gases into the UT/LS. Ultimately, these results can be used to reduce uncertainty in climate prediction models, a critical need considering the climate challenges facing humanity.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。对流层上部和平流层下部（UT/LS）的气体对全球大气的化学和辐射收支有重大影响，对理解气候变化至关重要。为了了解UT/LS区域的化学组成，需要更好地了解深对流输送。深对流中的输送是独特的，它能够从近地表夹带成分，并迅速将它们输送到UT/LS区域，在那里它们被去除。在目前的气候模式对流输送参数化中，风暴卷吸和消散的变化没有被捕获。首先，使用双多普勒速度数据从外地活动，观测到的detrainment配置文件将推导出一套风暴，涵盖了一系列的风暴形态和背景环境。第二，雷达反射率数据从相同的领域活动将被用来寻找速度派生的detrainment配置文件和反射率字段之间的关系。该提案第二部分的目标是确定一种处理雷达反射率观测的算法，以便根据天气雷达网覆盖的任何区域的雷达反射率观测来估计对流物质输送。第三，云解析模型将用于评估基于反射率的特定风暴传输估计的质量。使用雷达反射率观测和模拟相结合，在美国中部对流输送的气候学将被编译，从这项研究中产生的深对流输送的理解和模拟的改进将直接受益于许多互补的模拟工作，并提高到UT/LS的气体对流输送的理解。最终，这些结果可用于减少气候预测模型的不确定性，考虑到人类面临的气候挑战，这是一个关键需求。